{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 机器学习01 - 线性回归\n",
    "- 2021.07 樊晓唯"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. 单变量线性回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus']=False #用来正常显示负号"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### （1）数据加载"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>人口</th>\n",
       "      <th>收益</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.1101</td>\n",
       "      <td>17.5920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.5277</td>\n",
       "      <td>9.1302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.5186</td>\n",
       "      <td>13.6620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.0032</td>\n",
       "      <td>11.8540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.8598</td>\n",
       "      <td>6.8233</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       人口       收益\n",
       "0  6.1101  17.5920\n",
       "1  5.5277   9.1302\n",
       "2  8.5186  13.6620\n",
       "3  7.0032  11.8540\n",
       "4  5.8598   6.8233"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = './data/regress_data1.csv'\n",
    "data = pd.read_csv(path)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 97 entries, 0 to 96\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   人口      97 non-null     float64\n",
      " 1   收益      97 non-null     float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 1.6 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>人口</th>\n",
       "      <th>收益</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>97.000000</td>\n",
       "      <td>97.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8.159800</td>\n",
       "      <td>5.839135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.869884</td>\n",
       "      <td>5.510262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5.026900</td>\n",
       "      <td>-2.680700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.707700</td>\n",
       "      <td>1.986900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.589400</td>\n",
       "      <td>4.562300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.578100</td>\n",
       "      <td>7.046700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>22.203000</td>\n",
       "      <td>24.147000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              人口         收益\n",
       "count  97.000000  97.000000\n",
       "mean    8.159800   5.839135\n",
       "std     3.869884   5.510262\n",
       "min     5.026900  -2.680700\n",
       "25%     5.707700   1.986900\n",
       "50%     6.589400   4.562300\n",
       "75%     8.578100   7.046700\n",
       "max    22.203000  24.147000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "看下数据长什么样子"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(kind='scatter', x='人口', y='收益', figsize=(8,4))\n",
    "plt.xlabel('人口', fontsize=12)\n",
    "plt.ylabel('收益', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### （2）特征和标签拆分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 拆分特征x和标签y\n",
    "x = data.iloc[:,:-1]\n",
    "y = data.iloc[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((97, 1), (97,))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 最后，处理完数据，再确认一遍数据的维度\n",
    "x.shape, y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### （3）建模及评价指标\n",
    "[sklearn.linear_model.LinearRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression  # 线性回归\n",
    "\n",
    "# 1-初始化模型\n",
    "lr = LinearRegression()\n",
    "\n",
    "# 2.1-训练\n",
    "lr.fit(x, y)\n",
    "\n",
    "# 2.2-输出训练参数组合\n",
    "print(\"训练得到参数组合为：\\n其中w:\\n\", lr.coef_, \"\\nw0:\\n\",lr.intercept_)\n",
    "# y = -3.89 + 1.19*x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**决定系数R2**  [官网](https://scikit-learn.org/0.16/modules/generated/sklearn.metrics.r2_score.html)\n",
    "- 决定系数（coefficient ofdetermination），也叫R2判定系数或拟合优度。\n",
    "- R2反应了**因变量y的波动有多少百分比能被自变量x的波动所描述，所以R2的值在0-1之间，越接近1越好**\n",
    "- 意义：R2值越大，说明x对y的解释程度越高。也说明观察点在回归直线附近越密集。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "在训练集上的决定系数r2得分为： 0.7020315537841397\n",
      "在训练集上的均方误差MSE为： 8.953942751950358\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "# 3-模型预测\n",
    "y_pred = lr.predict(x)  # 预测\n",
    "\n",
    "# 4-模型评估\n",
    "print(\"在训练集上的决定系数r2得分为：\", r2_score(y, y_pred))    # 也可以直接使用：lr.score(x, y)\n",
    "print(\"在训练集上的均方误差MSE为：\", mean_squared_error(y, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### （4）可视化"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "现在我们来绘制线性模型以及数据，直观地看出它的拟合。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt_x = np.linspace(x.min(), x.max(), 100)   # 横轴选取100个点\n",
    "f = lr.intercept_ + (lr.coef_ * plt_x)   # y = -3.89 + 1.19*x 预测的表达式\n",
    "\n",
    "plt.figure(figsize=(8, 4))\n",
    "\n",
    "plt.plot(plt_x, f, 'r', label='预测值')  # 预测的直线\n",
    "plt.scatter(data['人口'], data['收益'], label='训练数据')   # 原始数据集\n",
    "\n",
    "plt.legend(loc=2)\n",
    "plt.title('预测收益和人口规模', fontsize=14)\n",
    "plt.xlabel('人口', fontsize=12)\n",
    "plt.ylabel('收益', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. 多变量线性回归"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### [Boston 房价回归数据集](https://scikit-learn.org/stable/datasets/toy_dataset.html#boston-dataset)\n",
    "\n",
    "- 数据集来自卡内基梅隆大学维护的 StatLib 库。\n",
    "- 样本包含 1970 年代的在波士顿郊区不同位置的房屋信息，总共有 13 种房屋属性。 目标值是**一个位置的房屋的中值（单位：k$）**。\n",
    "- 这十三个特征包括：\n",
    "    1. 人均犯罪率\n",
    "    1. 在25000平方英尺上住房的比例\n",
    "    1. 每个镇上平均的非零售企业比例\n",
    "    1. 是否沿河（查尔斯河）1/0\n",
    "    1. 一氧化氮的浓度，单位是千万分之一\n",
    "    1. 每个住所的平均房间数\n",
    "    1. 1940年以前自己修的建筑比例\n",
    "    1. 波士顿五个就业中心的加权距离\n",
    "    1. 径向公路适应性指标\n",
    "    1. 每10000的税率的完全价值\n",
    "    1. 镇上的学生-老师比例\n",
    "    1. 其中Bk是比例\n",
    "    1. 低地位人口比例\n",
    "- 而标签则是有房子人的房价的中位数。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### （1）数据加载"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_boston\n",
    "\n",
    "# 导入波士顿房价数据集\n",
    "dataset = load_boston()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = dataset.data # 导入所有特征变量\n",
    "y = dataset.target # 导入目标值（房价）\n",
    "names = dataset.feature_names #导入特征名"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0.,  18.,   2.,   0.,   1.,   7.,  65.,   4.,   1., 296.,  15.,\n",
       "        397.,   5.],\n",
       "       [  0.,   0.,   7.,   0.,   0.,   6.,  79.,   5.,   2., 242.,  18.,\n",
       "        397.,   9.],\n",
       "       [  0.,   0.,   7.,   0.,   0.,   7.,  61.,   5.,   2., 242.,  18.,\n",
       "        393.,   4.],\n",
       "       [  0.,   0.,   2.,   0.,   0.,   7.,  46.,   6.,   3., 222.,  19.,\n",
       "        395.,   3.],\n",
       "       [  0.,   0.,   2.,   0.,   0.,   7.,  54.,   6.,   3., 222.,  19.,\n",
       "        397.,   5.],\n",
       "       [  0.,   0.,   2.,   0.,   0.,   6.,  59.,   6.,   3., 222.,  19.,\n",
       "        394.,   5.],\n",
       "       [  0.,  12.,   8.,   0.,   1.,   6.,  67.,   6.,   5., 311.,  15.,\n",
       "        396.,  12.],\n",
       "       [  0.,  12.,   8.,   0.,   1.,   6.,  96.,   6.,   5., 311.,  15.,\n",
       "        397.,  19.],\n",
       "       [  0.,  12.,   8.,   0.,   1.,   6., 100.,   6.,   5., 311.,  15.,\n",
       "        387.,  30.],\n",
       "       [  0.,  12.,   8.,   0.,   1.,   6.,  86.,   7.,   5., 311.,  15.,\n",
       "        387.,  17.]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看前10个样本的特征\n",
    "X[:10].round(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([24., 22., 35., 33., 36., 29., 23., 27., 16., 19.])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看前10个样本的标签\n",
    "y[:10].round(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD',\n",
       "       'TAX', 'PTRATIO', 'B', 'LSTAT'], dtype='<U7')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看列名\n",
    "names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50.0, 5.0)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看房价最大最小值\n",
    "np.max(y),np.min(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((506, 13), (506,))"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看数据维度\n",
    "X.shape, y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 21.,  55.,  82., 154.,  84.,  41.,  30.,   8.,  10.,  21.]),\n",
       " array([ 5. ,  9.5, 14. , 18.5, 23. , 27.5, 32. , 36.5, 41. , 45.5, 50. ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 查看房价的直方图，房价分布\n",
    "plt.hist(y, bins = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x2160 with 13 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 查看每一个特征对标签y的二维关系，散点图\n",
    "\n",
    "plt.figure(figsize=(10,30))\n",
    "\n",
    "for i in range(13):\n",
    "    plt.subplot(7,2,i+1)  # 子图，7行2列，从第一个子图开始画\n",
    "    plt.scatter(X[:,i],y)  # 散点图\n",
    "    plt.title(names[i])     # 显示列名\n",
    "    plt.show"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### （2）数据拆分\n",
    "- 数据需要被分割为训练集和测试集："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(404, 13)\n",
      "(102, 13)\n",
      "(404,)\n",
      "(102,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split  # 拆分训练集和测试集\n",
    "\n",
    "#随机20%的数据构建测试样本，剩余作为训练样本\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,\n",
    "                                                    y,\n",
    "                                                    random_state=75,  # 随机种子，自己设置\n",
    "                                                    test_size=0.20)   #测试集占比20%\n",
    "print(X_train.shape) \n",
    "print(X_test.shape)\n",
    "print(y_train.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### （3）数据标准化\n",
    "\n",
    "- StandardScaler标准化方法，针对**每一个特征维度**来做的，而不是针对样本。 \n",
    "- 使得经过处理的数据符合标准正态分布，即均值为0，标准差为1，其转化函数为： **x_std = (x-μ)/σ**\n",
    "- 其中μ为所有样本数据的均值，σ为所有样本数据的标准差。\n",
    "- **用于测试集的标准化的均值μ和标准差σ应该都来源于训练集，即在train上面fit，然后transform到test上**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'> <class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "# 特征数据标准化 用均值-标准差标准化\n",
    "from sklearn.preprocessing import StandardScaler   # z-score标准化\n",
    "\n",
    "sc = StandardScaler()  # 初始化\n",
    "X_train_std = sc.fit_transform(X_train)  # 用train fit和transform\n",
    "X_test_std = sc.transform(X_test)        # test只做transform\n",
    "\n",
    "print(type(X_train_std),type(X_test_std))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "在进行数据的标准化后：\n",
      "train数据预览：------------\n",
      "[[-0.4   3.09 -1.38 -0.26 -1.31  1.18 -1.15  1.68 -0.86 -0.45 -2.71 -0.05\n",
      "  -0.55]\n",
      " [-0.39 -0.5  -1.1  -0.26 -0.55  1.14 -0.2  -0.19 -0.86 -0.81 -0.31  0.42\n",
      "  -0.96]\n",
      " [ 0.26 -0.5   1.05 -0.26 -0.17 -0.06 -0.14 -0.19  1.72  1.57  0.8   0.43\n",
      "  -0.25]]\n",
      "train的最大值是：\n",
      " [9.7  3.98 2.46 3.81 2.84 3.5  1.12 3.31 1.72 1.84 1.63 0.43 3.61]\n",
      "train的最小值是：\n",
      " [-0.4  -0.5  -1.53 -0.26 -1.48 -3.89 -2.35 -1.29 -0.97 -1.3  -2.71 -4.07\n",
      " -1.52]\n",
      "train的平均值是：\n",
      " [-0. -0.  0. -0. -0. -0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "train的标准差是：\n",
      " [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n"
     ]
    }
   ],
   "source": [
    "print(\"在进行数据的标准化后：\\ntrain数据预览：------------\")\n",
    "print(X_train_std[:3].round(2))\n",
    "print(\"train的最大值是：\\n\",np.max(X_train_std, axis=0).round(2))\n",
    "print(\"train的最小值是：\\n\",np.min(X_train_std, axis=0).round(2))\n",
    "print(\"train的平均值是：\\n\",np.mean(X_train_std, axis=0).round(2))\n",
    "print(\"train的标准差是：\\n\",np.std(X_train_std, axis=0).round(2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "在进行数据的标准化后：\n",
      "test数据预览：------------\n",
      "[[-0.39 -0.5  -1.24 -0.26 -0.56  2.12  0.52 -0.52 -0.74 -1.27 -0.31  0.42\n",
      "  -0.7 ]\n",
      " [-0.4  -0.5   0.15 -0.26  0.19 -0.23  0.29 -0.74 -0.97 -0.79  1.17  0.43\n",
      "  -0.48]\n",
      " [-0.39 -0.5   2.16 -0.26  0.26 -0.4   0.55 -0.78 -0.86 -1.3   0.29  0.21\n",
      "   0.25]]\n",
      "test的最大值是：\n",
      " [4.79 3.54 2.16 3.81 2.84 3.58 1.12 3.99 1.72 1.57 1.26 0.43 3.47]\n",
      "test的最小值是：\n",
      " [-0.4  -0.5  -1.49 -0.26 -1.44 -2.52 -2.22 -1.27 -0.97 -1.31 -2.71 -3.99\n",
      " -1.42]\n",
      "test的平均值是：\n",
      " [ 0.05  0.07  0.15  0.1   0.15  0.03 -0.01 -0.06  0.13  0.11 -0.03 -0.12\n",
      "  0.12]\n",
      "test的标准差是：\n",
      " [0.87 1.2  1.01 1.16 1.12 1.02 1.03 1.05 1.08 1.05 1.   1.16 1.05]\n"
     ]
    }
   ],
   "source": [
    "print(\"在进行数据的标准化后：\\ntest数据预览：------------\")\n",
    "print(X_test_std[:3].round(2))\n",
    "print(\"test的最大值是：\\n\",np.max(X_test_std, axis=0).round(2))\n",
    "print(\"test的最小值是：\\n\",np.min(X_test_std, axis=0).round(2))\n",
    "print(\"test的平均值是：\\n\",np.mean(X_test_std, axis=0).round(2))\n",
    "print(\"test的标准差是：\\n\",np.std(X_test_std, axis=0).round(2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((404, 13), (404,), (102, 13), (102,))"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 最后，处理完数据，再确认一遍数据的维度\n",
    "X_train_std.shape, y_train.shape, X_test_std.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### （4）建模"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "在训练集训练得到参数组合为：\n",
      "其中w:\n",
      " [-1.16244159  0.88819464  0.32990001  0.94048784 -1.92124141  2.84889188\n",
      " -0.05701888 -2.93668987  2.7177249  -1.69997015 -1.93243704  0.73843612\n",
      " -3.93577451] \n",
      "b:\n",
      " 22.686881188118857\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression  # 线性回归\n",
    "\n",
    "# 初始化模型\n",
    "lr = LinearRegression()\n",
    "\n",
    "# 训练\n",
    "lr.fit(X_train_std, y_train)\n",
    "\n",
    "# 输出训练最优组合\n",
    "print(\"在训练集训练得到参数组合为：\\n其中w:\\n\", lr.coef_, \"\\nb:\\n\",lr.intercept_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**决定系数R2**\n",
    "- 决定系数（coefficient ofdetermination），也叫R2判定系数或拟合优度。\n",
    "- R2反应了**因变量y的波动有多少百分比能被自变量x的波动所描述，所以R2的值在0-1之间，越接近1越好**\n",
    "- 意义：R2值越大，说明x对y的解释程度越高。也说明观察点在回归直线附近越密集。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "在训练集上的决定系数r2得分为： 0.7403155273951003\n",
      "在训练集上的均方误差MSE为： 22.51502974715272\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "y_train_pred = lr.predict(X_train_std)\n",
    "print(\"在训练集上的决定系数r2得分为：\", r2_score(y_train, y_train_pred))\n",
    "print(\"在训练集上的均方误差MSE为：\", mean_squared_error(y_train, y_train_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "在测试集上的决定系数r2得分为： 0.7140709040092728\n",
      "在测试集上的均方误差MSE为： 21.420344972184722\n"
     ]
    }
   ],
   "source": [
    "y_test_pred = lr.predict(X_test_std)\n",
    "print(\"在测试集上的决定系数r2得分为：\", r2_score(y_test, y_test_pred))\n",
    "print(\"在测试集上的均方误差MSE为：\", mean_squared_error(y_test, y_test_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### L2正则化\n",
    "$J (  { w } ) = \\frac { 1 } { 2 } \\sum _ { i = 1 } ^ { m } ( h _ { w} ( x ^ { ( i ) } ) - y ^ { ( i ) } ) ^ { 2 } + \\lambda \\sum _ { j = 1 } ^ { n } w_ { j } ^ { 2 }$，此时称作`Ridge Regression`："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "L2正则化在训练集训练得到参数组合为：\n",
      "其中w:\n",
      " [-1.07517913  0.77987335  0.10739345  0.97565825 -1.64587106  2.90686431\n",
      " -0.12387931 -2.69261997  2.12627988 -1.1735825  -1.85160043  0.72611488\n",
      " -3.7954732 ] \n",
      "b:\n",
      " 22.686881188118857\n",
      "在训练集上的决定系数r2得分为： 0.7392683578748679\n",
      "在训练集上的均方误差MSE为： 22.60582090097814\n",
      "在测试集上的决定系数r2得分为： 0.7152511920222212\n",
      "在测试集上的均方误差MSE为： 21.331923832963867\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import Ridge   # L2正则化\n",
    "# alpha：正则化系数，float类型，默认为1.\n",
    "l2model = Ridge(10)\n",
    "l2model.fit(X_train_std, y_train)\n",
    "\n",
    "# 输出训练组合\n",
    "print(\"L2正则化在训练集训练得到参数组合为：\\n其中w:\\n\", l2model.coef_, \"\\nb:\\n\",l2model.intercept_)\n",
    "\n",
    "y_train_pred = l2model.predict(X_train_std)\n",
    "print(\"在训练集上的决定系数r2得分为：\", r2_score(y_train, y_train_pred))\n",
    "print(\"在训练集上的均方误差MSE为：\", mean_squared_error(y_train, y_train_pred))\n",
    "\n",
    "y_test_pred = l2model.predict(X_test_std)\n",
    "print(\"在测试集上的决定系数r2得分为：\", r2_score(y_test, y_test_pred))\n",
    "print(\"在测试集上的均方误差MSE为：\", mean_squared_error(y_test, y_test_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## L1正则化：\n",
    "$J (  {w } ) = \\frac { 1 } { 2 } \\sum _ { i = 1 } ^ { m } ( h _ { w} ( x ^ { ( i ) } ) - y ^ { ( i ) } ) ^ { 2 } + \\lambda \\sum _ { j = 1 } ^ { n } | w _ { j } |$，此时称作`Lasso Regression` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "在训练集训练得到参数组合为：\n",
      "其中w:\n",
      " [-0.          0.         -0.          0.40349086 -0.          2.91322255\n",
      " -0.         -0.         -0.         -0.         -1.09965438  0.\n",
      " -3.55359532] \n",
      "b:\n",
      " 22.686881188118857\n",
      "在训练集上的决定系数r2得分为： 0.6633698645530317\n",
      "在训练集上的均方误差MSE为： 29.18633308086953\n",
      "在测试集上的决定系数r2得分为： 0.6731423136014891\n",
      "在测试集上的均方误差MSE为： 24.48650556253055\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import Lasso   # L1正则化\n",
    "\n",
    "l1model = Lasso(alpha=1) \n",
    "l1model.fit(X_train_std, y_train)\n",
    "\n",
    "# 输出训练组合\n",
    "print(\"在训练集训练得到参数组合为：\\n其中w:\\n\", l1model.coef_, \"\\nb:\\n\",l1model.intercept_)\n",
    "\n",
    "y_train_pred = l1model.predict(X_train_std)\n",
    "print(\"在训练集上的决定系数r2得分为：\", r2_score(y_train, y_train_pred))\n",
    "print(\"在训练集上的均方误差MSE为：\", mean_squared_error(y_train, y_train_pred))\n",
    "\n",
    "y_test_pred = l1model.predict(X_test_std)\n",
    "print(\"在测试集上的决定系数r2得分为：\", r2_score(y_test, y_test_pred))\n",
    "print(\"在测试集上的均方误差MSE为：\", mean_squared_error(y_test, y_test_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 调参（选修）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "alphas = np.logspace(-3, 2, 50)\n",
    "test_scores = []\n",
    "for alpha in alphas:\n",
    "    clf = Ridge(alpha)\n",
    "    test_score = np.sqrt(-cross_val_score(clf, X_train_std, y_train, cv=5, scoring='neg_mean_squared_error'))\n",
    "    test_scores.append(np.mean(test_score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.00000000e-03, 1.26485522e-03, 1.59985872e-03, 2.02358965e-03,\n",
       "       2.55954792e-03, 3.23745754e-03, 4.09491506e-03, 5.17947468e-03,\n",
       "       6.55128557e-03, 8.28642773e-03, 1.04811313e-02, 1.32571137e-02,\n",
       "       1.67683294e-02, 2.12095089e-02, 2.68269580e-02, 3.39322177e-02,\n",
       "       4.29193426e-02, 5.42867544e-02, 6.86648845e-02, 8.68511374e-02,\n",
       "       1.09854114e-01, 1.38949549e-01, 1.75751062e-01, 2.22299648e-01,\n",
       "       2.81176870e-01, 3.55648031e-01, 4.49843267e-01, 5.68986603e-01,\n",
       "       7.19685673e-01, 9.10298178e-01, 1.15139540e+00, 1.45634848e+00,\n",
       "       1.84206997e+00, 2.32995181e+00, 2.94705170e+00, 3.72759372e+00,\n",
       "       4.71486636e+00, 5.96362332e+00, 7.54312006e+00, 9.54095476e+00,\n",
       "       1.20679264e+01, 1.52641797e+01, 1.93069773e+01, 2.44205309e+01,\n",
       "       3.08884360e+01, 3.90693994e+01, 4.94171336e+01, 6.25055193e+01,\n",
       "       7.90604321e+01, 1.00000000e+02])"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alphas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot(alphas, test_scores)\n",
    "plt.title(\"Alpha vs CV Error\");\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4.959130108900107, 9.540954763499943)"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.min(test_scores), alphas[np.argmin(test_scores)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
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    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
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  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": false,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
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   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "232.727px"
   },
   "toc_section_display": true,
   "toc_window_display": true
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  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
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   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
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   "window_display": false
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 },
 "nbformat": 4,
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}
